February 2015
Beginner to intermediate
498 pages
16h 57m
English
Content preview from Big Data
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Chapter 1
Scalable Indexing for Big Data Processing
Hisham Mohamed and Stéphane Marchand-Maillet
Abstract
The K-nearest neighbor (K-NN) search problem is the way to find and predict the closest and most similar objects to a given query. It finds many applications for information retrieval and visualization, machine learning, and data mining. The context of Big Data imposes the finding of approximate solutions. Permutation-based indexing is one of the most recent techniques for approximate similarity search in large-scale domains. Data objects are represented by a list of references (pivots), which are ordered with respect to their distances from the object. In this chapter, we show different distributed algorithms for efficient indexing and ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.